Matteo Monchiero
Staff Software Engineer at Snowflake
United States
Summary
🤩
Rockstar🎓
Top SchoolMatteo Monchiero is a Staff Software Engineer and seasoned engineering leader with over 15 years of experience bridging research-grade systems and production software across companies like Snowflake, Streamlit, Trifacta, and Intel. He combines deep academic roots—a PhD from Politecnico di Milano—with hands-on full-stack and ML work, contributing notable features to the popular Streamlit open-source project (multiselect, enhanced st.dataframe, magic control flow support, and YOLOv3 integration in demo apps). Matteo has led and scaled remote engineering teams while continuing to ship code and improve developer UX, demonstrating a rare mix of people leadership and individual technical impact. He is comfortable across performance architecture, data apps, and real-time ML pipelines, and has supplemented his technical leadership with formal management training from Harvard Business School Online.
10 years of coding experience
21 years of employment as a software developer
PhD Electrical and Computer Engineering, PhD Electrical and Computer Engineering at Politecnico di MilanoCertificate Management Essential, Certificate Management Essential at Harvard Business School Online
Stackoverflow
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Github Skills (19)
opencv10
python10
image-processing10
machine-learning10
front-end-development10
computer-vision10
streamlit10
yolov410
testing9
py9
deep-learning9
react9
developer-tools9
data-visualization8
backend8
Programming languages (3)
Github contributions (5)
streamlit/demo-self-driving
Sep 2019 - Sep 2019
Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
Role in this project:
ML Engineer
ML EngineerContributions:63 commits, 2 PRs, 32 pushes in 24 days
Contributions summary:Matteo focused on implementing and integrating a YOLOv3 object detection model within a Streamlit application. Their commits show the integration of the YOLOv3 model, including loading weights, configuration, and applying object detection on images. The user also updated the application to include features like progress bars and downloadable weights for the model.
self-driving-carrealtimeimage-browserudacity-self-driving-carbrowser
streamlit/streamlit
Aug 2019 - Mar 2020
Streamlit — A faster way to build and share data apps.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:4 reviews, 35 commits, 60 PRs in 6 months
Contributions summary:Matteo made several contributions to the Streamlit library. They improved the handling of empty documentation in the `st.help` function and added support for `if` and `for` statements within Streamlit's "magic" feature. Furthermore, the user implemented a multiselect widget and enhanced the `st.dataframe` function by adding width and height parameters, along with corresponding end-to-end tests. The commits also include fixes for the spinner code and the ability to run Streamlit apps from a URL.
data-analysispythondata-sciencedeep-learningfastest
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